#ifdef USE_OPENCV
#include <string>
#include <vector>

#include "gtest/gtest.h"

#include "caffe/blob.hpp"
#include "caffe/common.hpp"
#include "caffe/data_transformer.hpp"
#include "caffe/filler.hpp"
#include "caffe/proto/caffe.pb.h"
#include "caffe/util/io.hpp"

#include "caffe/test/test_caffe_main.hpp"

namespace caffe {

void FillDatum(const int label, const int channels, const int height,
  const int width, const bool unique_pixels, Datum * datum) {
  datum->set_label(label);
  datum->set_channels(channels);
  datum->set_height(height);
  datum->set_width(width);
  int size = channels * height * width;
  std::string* data = datum->mutable_data();
  for (int j = 0; j < size; ++j) {
    int datum = unique_pixels ? j : label;
    data->push_back(static_cast<uint8_t>(datum));
  }
}

template <typename Dtype>
class DataTransformTest : public ::testing::Test {
 protected:
  DataTransformTest()
      : seed_(1701),
      num_iter_(10) {}

  int NumSequenceMatches(const TransformationParameter transform_param,
      const Datum& datum, Phase phase) {
    // Get crop sequence with Caffe seed 1701.
    DataTransformer<Dtype> transformer(transform_param, phase);
    const int crop_size = transform_param.crop_size();
    Caffe::set_random_seed(seed_);
    transformer.InitRand();
    Blob<Dtype> blob(1, datum.channels(), datum.height(), datum.width());
    if (transform_param.crop_size() > 0) {
      blob.Reshape(1, datum.channels(), crop_size, crop_size);
    }

    vector<vector<Dtype> > crop_sequence;
    for (int iter = 0; iter < this->num_iter_; ++iter) {
      vector<Dtype> iter_crop_sequence;
      transformer.Transform(datum, &blob);
      for (int j = 0; j < blob.count(); ++j) {
        iter_crop_sequence.push_back(blob.cpu_data()[j]);
      }
      crop_sequence.push_back(iter_crop_sequence);
    }
    // Check if the sequence differs from the previous
    int num_sequence_matches = 0;
    for (int iter = 0; iter < this->num_iter_; ++iter) {
      vector<Dtype> iter_crop_sequence = crop_sequence[iter];
      transformer.Transform(datum, &blob);
      for (int j = 0; j < blob.count(); ++j) {
        num_sequence_matches += (crop_sequence[iter][j] == blob.cpu_data()[j]);
      }
    }
    return num_sequence_matches;
  }

  int seed_;
  int num_iter_;
};

TYPED_TEST_CASE(DataTransformTest, TestDtypes);

TYPED_TEST(DataTransformTest, TestEmptyTransform) {
  TransformationParameter transform_param;
  const bool unique_pixels = false;  // all pixels the same equal to label
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;

  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  Blob<TypeParam> blob(1, channels, height, width);
  DataTransformer<TypeParam> transformer(transform_param, TEST);
  transformer.InitRand();
  transformer.Transform(datum, &blob);
  EXPECT_EQ(blob.num(), 1);
  EXPECT_EQ(blob.channels(), datum.channels());
  EXPECT_EQ(blob.height(), datum.height());
  EXPECT_EQ(blob.width(), datum.width());
  for (int j = 0; j < blob.count(); ++j) {
    EXPECT_EQ(blob.cpu_data()[j], label);
  }
}

TYPED_TEST(DataTransformTest, TestEmptyTransformUniquePixels) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;

  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  Blob<TypeParam> blob(1, 3, 4, 5);
  DataTransformer<TypeParam> transformer(transform_param, TEST);
  transformer.InitRand();
  transformer.Transform(datum, &blob);
  EXPECT_EQ(blob.num(), 1);
  EXPECT_EQ(blob.channels(), datum.channels());
  EXPECT_EQ(blob.height(), datum.height());
  EXPECT_EQ(blob.width(), datum.width());
  for (int j = 0; j < blob.count(); ++j) {
    EXPECT_EQ(blob.cpu_data()[j], j);
  }
}

TYPED_TEST(DataTransformTest, TestCropSize) {
  TransformationParameter transform_param;
  const bool unique_pixels = false;  // all pixels the same equal to label
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int crop_size = 2;

  transform_param.set_crop_size(crop_size);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  DataTransformer<TypeParam> transformer(transform_param, TEST);
  transformer.InitRand();
  Blob<TypeParam> blob(1, channels, crop_size, crop_size);
  for (int iter = 0; iter < this->num_iter_; ++iter) {
    transformer.Transform(datum, &blob);
    EXPECT_EQ(blob.num(), 1);
    EXPECT_EQ(blob.channels(), datum.channels());
    EXPECT_EQ(blob.height(), crop_size);
    EXPECT_EQ(blob.width(), crop_size);
    for (int j = 0; j < blob.count(); ++j) {
      EXPECT_EQ(blob.cpu_data()[j], label);
    }
  }
}

TYPED_TEST(DataTransformTest, TestCropTrain) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int crop_size = 2;
  const int size = channels * crop_size * crop_size;

  transform_param.set_crop_size(crop_size);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  int num_matches = this->NumSequenceMatches(transform_param, datum, TRAIN);
  EXPECT_LT(num_matches, size * this->num_iter_);
}

TYPED_TEST(DataTransformTest, TestCropTest) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int crop_size = 2;
  const int size = channels * crop_size * crop_size;

  transform_param.set_crop_size(crop_size);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
  EXPECT_EQ(num_matches, size * this->num_iter_);
}

TYPED_TEST(DataTransformTest, TestMirrorTrain) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int size = channels * height * width;

  transform_param.set_mirror(true);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  int num_matches = this->NumSequenceMatches(transform_param, datum, TRAIN);
  EXPECT_LT(num_matches, size * this->num_iter_);
}

TYPED_TEST(DataTransformTest, TestMirrorTest) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int size = channels * height * width;

  transform_param.set_mirror(true);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  int num_matches = this->NumSequenceMatches(transform_param, datum, TEST);
  EXPECT_LT(num_matches, size * this->num_iter_);
}

TYPED_TEST(DataTransformTest, TestCropMirrorTrain) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int crop_size = 2;

  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  transform_param.set_crop_size(crop_size);
  int num_matches_crop = this->NumSequenceMatches(
      transform_param, datum, TRAIN);

  transform_param.set_mirror(true);
  int num_matches_crop_mirror =
      this->NumSequenceMatches(transform_param, datum, TRAIN);
  // When doing crop and mirror we expect less num_matches than just crop
  EXPECT_LE(num_matches_crop_mirror, num_matches_crop);
}

TYPED_TEST(DataTransformTest, TestCropMirrorTest) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int crop_size = 2;

  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  transform_param.set_crop_size(crop_size);
  int num_matches_crop = this->NumSequenceMatches(transform_param, datum, TEST);

  transform_param.set_mirror(true);
  int num_matches_crop_mirror =
      this->NumSequenceMatches(transform_param, datum, TEST);
  // When doing crop and mirror we expect less num_matches than just crop
  EXPECT_LT(num_matches_crop_mirror, num_matches_crop);
}


TYPED_TEST(DataTransformTest, TestMeanValue) {
  TransformationParameter transform_param;
  const bool unique_pixels = false;  // pixels are equal to label
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int mean_value = 2;

  transform_param.add_mean_value(mean_value);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  Blob<TypeParam> blob(1, channels, height, width);
  DataTransformer<TypeParam> transformer(transform_param, TEST);
  transformer.InitRand();
  transformer.Transform(datum, &blob);
  for (int j = 0; j < blob.count(); ++j) {
    EXPECT_EQ(blob.cpu_data()[j], label - mean_value);
  }
}

TYPED_TEST(DataTransformTest, TestMeanValues) {
  TransformationParameter transform_param;
  const bool unique_pixels = false;  // pixels are equal to label
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;

  transform_param.add_mean_value(0);
  transform_param.add_mean_value(1);
  transform_param.add_mean_value(2);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  Blob<TypeParam> blob(1, channels, height, width);
  DataTransformer<TypeParam> transformer(transform_param, TEST);
  transformer.InitRand();
  transformer.Transform(datum, &blob);
  for (int c = 0; c < channels; ++c) {
    for (int j = 0; j < height * width; ++j) {
      EXPECT_EQ(blob.cpu_data()[blob.offset(0, c) + j], label - c);
    }
  }
}

TYPED_TEST(DataTransformTest, TestMeanFile) {
  TransformationParameter transform_param;
  const bool unique_pixels = true;  // pixels are consecutive ints [0,size]
  const int label = 0;
  const int channels = 3;
  const int height = 4;
  const int width = 5;
  const int size = channels * height * width;

  // Create a mean file
  string mean_file;
  MakeTempFilename(&mean_file);
  BlobProto blob_mean;
  blob_mean.set_num(1);
  blob_mean.set_channels(channels);
  blob_mean.set_height(height);
  blob_mean.set_width(width);

  for (int j = 0; j < size; ++j) {
      blob_mean.add_data(j);
  }

  LOG(INFO) << "Using temporary mean_file " << mean_file;
  WriteProtoToBinaryFile(blob_mean, mean_file);

  transform_param.set_mean_file(mean_file);
  Datum datum;
  FillDatum(label, channels, height, width, unique_pixels, &datum);
  Blob<TypeParam> blob(1, channels, height, width);
  DataTransformer<TypeParam> transformer(transform_param, TEST);
  transformer.InitRand();
  transformer.Transform(datum, &blob);
  for (int j = 0; j < blob.count(); ++j) {
    EXPECT_EQ(blob.cpu_data()[j], 0);
  }
}

}  // namespace caffe
#endif  // USE_OPENCV